Book review: Complex Adaptive Systems, by John Miller and Scott Page

Miller and Page's book provides an introduction into the computational, agent based, modeling of complex adaptive systems, and gives the reader what I hope to be an overview about the present day status of ideas and concepts. It's a bit hard to tell for me, since it's the first book I read on the topic.

They motivate the possible importance of these studies for the understanding of social, political, and economical dynamics. Though the motivation is plenty, the book lacks some convincing examples where this modeling has actually proved to be useful. Maybe there just are none? I am left with a feeling of an area that has a vast potential, but whose actual relevance is presently more or less unclear.

The main theme of the book is the 'Interest in Between'. In between not only in between computational modeling an social life, but also in between very few and infinitely many agents, in between equilibrium and chaos, in between omniscient and utterly stupid agents, in between uniformity and individualism (call it homogeneity and heterogeneity) - cases in which mathematical proof of a system's behavior is only rarely possible, and numerical simulations become increasingly important.

The book is well structured, and after some examples to catch the reader's interest, it starts with a general discussion about how reducing the study of a system to a study of its constituents does not shed light on emerging features. They continue with a chapter on what makes a good model. A chapter that I find unnecessarily defensive, but it seems to be aimed at a different audience than theoretical physicists.

In the following chapters the examples become gradually more sophisticated, and illuminate the importance of various ingredients to the modeling, such as the order of updating, the intelligence of the agents, or communication between them, and selection rules that allow the complex system to 'adapt', and the relevance of living on 'the edge of chaos'. In several places, the authors provide a mathematical proof for features of certain models that they discuss. I admit on not actually following these proofs since I don't presently need them, but it's useful to know they are there.

However, in several subsections the discussion gets lost into details that the reader can't follow merely from what was explained earlier, and it requires more knowledge than what the authors provide. As an example, in Section 10.3.2 I learned

"The four games with the highest variations consisted of all the games with a single, iterated dominant strategy equilibrium that was Pareto dominated by a non-Nash outcome. Besides the Prisoner's Dilemma, two other symmetric games had above-average measures of outcome variation: Chicken, which was fifth, and Battle of the Sexes which was sixteenth."

I am either a very sloppy reader or besides the Prisoner's Dilemma indeed none of the other games was previously explained. Neither do I have any clue what Pareto dominated means or what a non-Nash outcome is. One can surely look this up elsewhere, but sections like this leave me with the impression of a reprint of a paper (which might actually be the case, I haven't yet checked the reference list.) Also, in various places, figures appear that are reprints from other publications, but what is actually shown on the figure is not well explained. E.g. the variables in figure 12.1 just remain undefined. Though this isn't of much relevance to the context, the only purpose for such figures seem to be to just have a figure.

As to the writing style, it is very mixed, and ranks from almost poetic paragraphs with a subtle sense of humor to alignments of very dry and technical explanations. It makes me wonder whether the writing was shared between the authors, or as speculated above, was filled up with pasting in extracts from papers in scientific journals. In several places the general motivation is somewhat repetitive.

Also, it is mentioned somewhere in the book that even though there is no exact definition for complexity, there are definitions that are useful in certain cases. Given that this is one of the main themes in the book, I would have liked to hear more about this.

The book has two appendices, the first is essentially an outlook with various open questions, which I find very interesting. The second one are 'practices for modeling' which are advices for how to write a good numerical code.

Taken together, it is a good book, but depending on your knowledge it either contains too much or too little information. It is however a useful book to have because it's the first of this kind. If this was an amazon review, I'd give three stars.

I admittedly haven't checked the literature too carefully, but from what I came across it's the first introduction to the use of such computational agent based models (that's also what the backflap says). There a surely lots of other books about complex systems etc, and there are also other sources besides books, but well, I had to start somewhere.

If you are stupid without a computer you are still stupid with a computer.

US Secretary of Defense Robert McNamara entered Vietnam. He modeled a Mil-Spec enemy that carried casebooks, eschewed heteroskedasticity, and surrendered when it could not win. Dien Bien Phu (1954) was insubordinate to his model. It lacked quantified negotiating table carpentry during his own defeat.

Game theory and adaptive programming suffer in the real world. Efforts and results lack 1:1 correspondence. Mobs are wildly non-linear, tolerances can add rather than average. An enemy with no hope remaining has nothing to lose by persisting.

The most perfect sine curve polynomial approximation is worthless for extrapolating one cycle more. Patching it for that cycle (heteroskedasticity conquered!) does not improve it for the cycle beyond that. Head Start, Welfare, Social Security, Medicare; Department of Energy, FEMA, Homeland Severity... and, of course, the Department of Defense. They are helpless and hopeless against an exponentially angry future that disdains actuaries.

I think the part in the book that I found overly defensive is written for people like you. A computer can't make you more intelligent, but it can help you examine features that otherwise would remain inaccessible due to the sheer complexity of the situation. Typically, if you have a large parameter space, or many (but not infinitely many) constituents, numerical models are often the way to go. One can hope that if in such a way one finds interesting behavior, it is subsequently possible to find mathematical prove under certain circumstances, and to show such behaviour is actually quite robust if one generalizes these circumstances.

The book spends quite some while on non-linear behavior and feedback effects which can lead to sudden change in a system's behavior. I think you are implicitly assuming the focus has been on making predictions of some kind, instead of understanding the behavior of the system (like e.g. its ability to 'adapt' or reach an equilibrium).

Nice review. I wonder sometimes if you have a different number of hours in a day then I do. That is you seem to find time be a researcher, write this blog and read. What I’m curious to hear your opinion on is if you think that such techniques will ever lead us to the point that in could help us to plan our future? I am reminded of Isaac Asimov’s classic science fiction collection the "Foundation Trilogy" where a scientist called Hari Seldon invented what was called “psychohistory” which allowed him to predict the future. He discovered that no matter what was done a collapse of society could not be avoided and the best that could be done was set in motion a delicate scheme which would allow its renewal after only a 1000 years, as apposed to the 30,000 his model predicted. This of course is only science fiction and yet with the actual attempt to build quantum computers this might not just remain to be in the realm of fantasy for very long.

A general arranges overall logistics and strategy. Old fart sergeants run local tactics. If their lieutenants don't listen everybody dies. Ex-corprorals Napolean and Hitler were incredible local tacticians but poor generals at global scales (e.g., going East; adding America). The Vietnamese knew how to wield a negotiation table.

Effective centralized control is a mathematical impossibility. The best one can do is set policy at the top and have each successive layer down act in accord with local conditions. Small problems can be real-world optimized in a fractal fashion. Take small bites and chew well. Big gulps will choke you, flesh or software.

Case I, "...one vast and ecumenical holding company, for whom all men will work to serve a common profit, in which all men will hold a share of stock; all necessities provided, all anxieties tranquilized, all boredom amused."

Case II, "All I know is that first you've got to get mad. You've got to say, 'I'm a HUMAN BEING, Goddamnit! My life has VALUE!'"

In 1965 the United States impressed an invasive, erosive, metastatic multi-$trillion War on Self, Case I. In 2008 it is surveillance of every individual, confiscation of earned value hard onto starvation, and a ~$50 billion/year Big Pharma bill for antidepressants, tranquilizers, and sleeping pills (plus 10X that in street drugs). It is overseen by unlimited onion layers of computerization, professional management, and mathematical modeling. Think Green.

Uncle Al votes for Case II. As the UK poster said, We Will Force You To Be Free is no better for sheaves of differential equations churning in silicon - Parliament, Congress, or infinite budgeted optimized parameterized real-time networked pilot-free semi-autonomous blood and sand in Iraq.

Adaptive systems can run a Wal-Mart but not a nation. Know the difference. Nature does not care, we do.

Your point serves more to indicate that we would not be able to control those required to execute the plan, rather then the possibility as not to conceive one. Mind you I’m not so disappointed, since most people’s concept of what a Utopia appears to me more like a social lobotomy. As an example, there would be no need for and therefore exist any Uncle Als. Now I ask; what kind of world would that prove to be? :-)

Very bright guys using the theories and math to decide when to fight a war: Vietnam and Iraq, what joy. The right in the U.S. blames the press, the draft and the anti-war movement for the lose in Vietnam ignoring the Soviet lose in Afghanistan.

We won near every battle in Vietnam BUT battles are only partly about battles in Vietnam and Iraq we had no real plan or idea what do after we won and in fact we still don't in Iraq.

Very bright people can follow stupid theories and ideas as easily as stupid people.

Theories aren't bright or stupid, they are useful or they aren't. One shouldn't confuse a theory with it's predictive power though. I would have the theory that you can't plan a war, you can only make sure to react to situations in the best possibly way, where 'best possible' is up to you to specify. Same applies to politics. I don't think you can 'predict' sociological trends, the system is far too complex. But you can set it up such that it finds its way. Democracy e.g. seems like a good choice for that. But I think that our democratic systems could need some update as they don't function optimally, and certainly not on a global level. Best,

I Agree wiht the statement about the book appear require some previous complexity science's knowledge. For this book, could be interesting read the paper "Can Game(s) Theory Explain Culture?The Emergence of Cultural Behavior Within Multiple Games" where Jena Bednar and Scott e. Page evolve a framework based in the ideias of the book in questions.